The non‐ergodic nature of mental health and psychiatric disorders: implications for biomarker and diagnostic research
Authors: Andreas Meyer‐Lindenberg
Journal: World Psychiatry (2023)

The ultimate goal of both diagnosis and biomarkers in psychiatry – as in all areas of medicine – is to lead to better outcomes through improved management, tailored as necessary to the individual patient. As Abi-Dargham et al show in their magisterial overview of the field1, despite a massive research effort spanning half a century, no biomarkers have been identified that have proven useful and valid enough to change the clinical practice of psychiatry (the authors do not cover dementias, a group of psychiatric disorders where biomarkers are in fact relevant for management). While the complexity of mental illness is such that even now the field may be regarded to be in its early stages, it is perhaps not premature to examine whether there are fundamental implicit assumptions in this search for valid biomarkers that need to be reconsidered or amended. Here I propose that the temporal structure of mental health and psychiatric disorders is insufficiently captured in the way diagnoses and biomarkers are defined and validated in psychiatry. Specifically, I will argue that the non-ergodic nature of the mind limits the usefulness of constructing biomarkers cross-sectionally. I will outline a research program that can help to address this issue in future investigations. Without going into mathematical details2, a system is called ergodic if the variation across components is asymptotically equivalent to the variation within components (i.e., across time). If this is true, the time average and the expectation value of an observable will be the same. This formulation comes from statistical mechanics and was meant to capture systems at thermodynamic equilibrium such as gases. However, it has been widely applied throughout science, and even to groups of human beings and their mental processes. In psychiatry, the ergodicity assumption implies that one can draw inferences on the way a specific person will behave or respond to treatment (the goal of predictive biomarkers) from a cross-sectional statistical analysis across individuals captured at one point in time: the group average will be the most likely outcome for the individual. Even though this assumption is rarely questioned, it is often obviously false: depending on how payouts are defined, one round of Russian roulette in 100 individuals may yield a good average profit, while a game of 100 rounds of Russian roulette in one individual means certain death. Much of current biomarker research directly hinges on the ability to make inferences from groups to individuals, because many biomarkers are derived from very large samples of participants assessed only once or at few time points. This is especially true for the largest multinational collaborations collecting genetic (e.g., Psychiatric GWAS Consortium) or neuroimaging (e.g., ENIGMA) data. Ergodicity assumptions are also implicit in much of the work to build up diagnostic systems in psychiatry, such as the DSM-5 and ICD-11, because these systems are meant to optimize reliability in cross-sections of clinical samples. What is worse, these two approaches are compounded because many biomarkers aim at cross-sectional predictions of diagnoses (e.g., polygenic risk scores) which are defined in a way that makes only the most rudimentary reference (through duration or exclusion cri teria) to the time course of mental illness. Therefore, even if rarely made explicit, the success of biomarkers so derived hinges on the presence of ergodicity. What is the evidence here? Unfortunately, a substantial body of work shows that ergodicity assumptions do in fact not hold for mental processes3. Cases in point are the so-called ecological fallacies, in which group data predict the opposite of what is found within individuals. For example, across individuals, higher stress is linked to reduced physical activity, whereas the opposite relationship holds for a given person. A systematic study showed that within-person variability of mood scores exceeded that across individuals several fold3. This is not surprising, because the human brain and mind clearly operate very far from equilibrium and exhibit a broad repertoire of dynamics. Thus, biomarker research needs to take this problem seriously and aim to address it in future work. Fortunately, the desired inference from the group to the individual is sometimes possible even in non-ergodic systems, when deviations from the underlying assumptions are addressed through experimental or statistical methods. In this sense, there is less a black-and-white dichotomy than a "non-ergodicity continuum"4. The two main deviations from ergodicity to consider are: a) heterogeneity across individuals and b) non-stationarity of the dynamics across time within individuals. Of these two, inter-individual heterogeneity is well recognized in psychiatry and the focus of much biomarker and clinical development work. For example, randomization in clinical studies is performed precisely to account for unobserved heterogeneity of study participants, in order to make observed differences between a treatment and a control condition applicable to individual responses4. Similarly, much biomarker work aims to identify relevant variation not captured by current categorical diagnoses1. To optimally address (non)ergodicity, further work on heterogeneity should also aim to capture inter-individual variation irrespective of diagnoses, since these are also potentially problematic. For example, we have found that dimensional descriptors of behavior and neuropsychology are linked to neurofunction across and beyond diagnoses5. Even greater transformative potential in biomarker research lies in methods and paradigms to address non-stationarity. The ubiquity of smartphones, the sensors that they contain and that are found in wearables, and new analysis methods now allow research to move out of the laboratory and to capture the time course of psychiatrically relevant parameters within individuals at unprecedented detail and scale6. This ecological momentary assessment (EMA) approach drives two qualitative advances in the field: a) it makes within-individual variability accessible and thus uncovers a critical and previously inaccessible dimension to future biomarkers related to mental health, and b) it enables characterizing the environmental context (physical, but especially social) that generates changes in mental status. For example, we have seen strong links between intra-individual reactivity to environmental exposures and mental health risk7. The ability to capture the course of symptoms and mental states is especially important for psychiatry, because many mental disorders – at least in their early phases – manifest in the context of life events and crises that overwhelm the individual homeostatic capacity. They may thus inherently reflect critical changes in the underlying mental dynamics that can be captured from EMA time series through methods from dynamical systems theory. This makes essential non-stationarities explicit and allows accounting for them in biomarker research. Even potentially more important, inter-individual variation that predicts such changes in dynamics can and should be sought. For example, we and others have found that variations in dopaminergic genes influence the dynamical repertoire of brain networks8. Finally, a life-span perspective on the temporal dimension of mental disorders is afforded by new machine learning approaches to normative modelling, which can characterize an individual's deviation from a normative developmental timeline as a biomarker supporting early intervention and prevention approaches9. Several of these methodological recommendations for future research dovetail with the perspective outlined by Abi-Dargham et al1. Taken together, they have the potential to generate significant advances in the predictivity and clinical applicability of biomarkers through addressing obstacles from group to individual inference. Patients, their relatives, clinicians and research are likely to benefit from this.